Intrusion Detection - A Data Mining Approach (Hardcover, 1st ed. 2020)

This book presents state-of-the-art research on intrusion detection using reinforcement learning, fuzzy and rough set theories, and genetic algorithm. Reinforcement learning is employed to incrementally learn the computer network behavior, while rough and fuzzy sets are utilized to handle the uncertainty involved in the detection of traffic anomaly to secure data resources from possible attack. Genetic algorithms make it possible to optimally select the network traffic parameters to reduce the risk of network intrusion. The book is unique in terms of its content, organization, and writing style. Primarily intended for graduate electrical and computer engineering students, it is also useful for doctoral students pursuing research in intrusion detection and practitioners interested in network security and administration. The book covers a wide range of applications, from general computer security to server, network, and cloud security.

General

Imprint:	Springer Verlag, Singapore
Country of origin:	Singapore
Series:	Cognitive Intelligence and Robotics
Release date:	2020
First published:	2020
Authors:	Nandita Sengupta • Jaya Sil
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	136
Edition:	1st ed. 2020
ISBN-13:	978-981-15-2715-9
Categories:	Books > Reference & Interdisciplinary > Communication studies > Coding theory & cryptology Books > Computing & IT > Computer communications & networking > Network security Books > Computing & IT > Applications of computing > Databases > Data mining
LSN:	981-15-2715-6
Barcode:	9789811527159

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